Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations3000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory486.9 B

Variable types

Numeric19
Text2
Categorical4

Alerts

Addiction_Level is highly overall correlated with Daily_Usage_HoursHigh correlation
Daily_Usage_Hours is highly overall correlated with Addiction_LevelHigh correlation
ID is uniformly distributed Uniform
ID has unique values Unique
Social_Interactions has 257 (8.6%) zeros Zeros
Exercise_Hours has 366 (12.2%) zeros Zeros
Screen_Time_Before_Bed has 89 (3.0%) zeros Zeros
Time_on_Gaming has 205 (6.8%) zeros Zeros
Time_on_Education has 250 (8.3%) zeros Zeros

Reproduction

Analysis started2025-07-19 00:13:25.812586
Analysis finished2025-07-19 00:14:54.237526
Duration1 minute and 28.42 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Uniform  Unique 

Distinct3000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1500.5
Minimum1
Maximum3000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:44:54.412838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile150.95
Q1750.75
median1500.5
Q32250.25
95-th percentile2850.05
Maximum3000
Range2999
Interquartile range (IQR)1499.5

Descriptive statistics

Standard deviation866.16973
Coefficient of variation (CV)0.57725407
Kurtosis-1.2
Mean1500.5
Median Absolute Deviation (MAD)750
Skewness0
Sum4501500
Variance750250
MonotonicityStrictly increasing
2025-07-19T05:44:54.732862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2004 1
 
< 0.1%
1995 1
 
< 0.1%
1996 1
 
< 0.1%
1997 1
 
< 0.1%
1998 1
 
< 0.1%
1999 1
 
< 0.1%
2000 1
 
< 0.1%
2001 1
 
< 0.1%
2002 1
 
< 0.1%
Other values (2990) 2990
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3000 1
< 0.1%
2999 1
< 0.1%
2998 1
< 0.1%
2997 1
< 0.1%
2996 1
< 0.1%
2995 1
< 0.1%
2994 1
< 0.1%
2993 1
< 0.1%
2992 1
< 0.1%
2991 1
< 0.1%

Name
Text

Distinct2933
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size206.1 KiB
2025-07-19T05:44:55.276913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length22
Mean length13.316667
Min length6

Characters and Unicode

Total characters39950
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2871 ?
Unique (%)95.7%

Sample

1st rowShannon Francis
2nd rowScott Rodriguez
3rd rowAdrian Knox
4th rowBrittany Hamilton
5th rowSteven Smith
ValueCountFrequency (%)
michael 77
 
1.3%
smith 68
 
1.1%
williams 54
 
0.9%
johnson 54
 
0.9%
robert 50
 
0.8%
brown 50
 
0.8%
john 49
 
0.8%
jones 45
 
0.7%
christopher 43
 
0.7%
james 43
 
0.7%
Other values (1294) 5603
91.3%
2025-07-19T05:44:56.073108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3749
 
9.4%
a 3628
 
9.1%
3136
 
7.8%
n 2945
 
7.4%
r 2824
 
7.1%
i 2483
 
6.2%
l 2131
 
5.3%
o 2110
 
5.3%
s 1830
 
4.6%
t 1384
 
3.5%
Other values (44) 13730
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3749
 
9.4%
a 3628
 
9.1%
3136
 
7.8%
n 2945
 
7.4%
r 2824
 
7.1%
i 2483
 
6.2%
l 2131
 
5.3%
o 2110
 
5.3%
s 1830
 
4.6%
t 1384
 
3.5%
Other values (44) 13730
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3749
 
9.4%
a 3628
 
9.1%
3136
 
7.8%
n 2945
 
7.4%
r 2824
 
7.1%
i 2483
 
6.2%
l 2131
 
5.3%
o 2110
 
5.3%
s 1830
 
4.6%
t 1384
 
3.5%
Other values (44) 13730
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3749
 
9.4%
a 3628
 
9.1%
3136
 
7.8%
n 2945
 
7.4%
r 2824
 
7.1%
i 2483
 
6.2%
l 2131
 
5.3%
o 2110
 
5.3%
s 1830
 
4.6%
t 1384
 
3.5%
Other values (44) 13730
34.4%

Age
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.969667
Minimum13
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:44:56.232363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile13
Q114
median16
Q318
95-th percentile19
Maximum19
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9894889
Coefficient of variation (CV)0.12457924
Kurtosis-1.2243572
Mean15.969667
Median Absolute Deviation (MAD)2
Skewness0.025138831
Sum47909
Variance3.9580659
MonotonicityNot monotonic
2025-07-19T05:44:56.407306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
16 467
15.6%
13 433
14.4%
15 431
14.4%
14 427
14.2%
19 421
14.0%
17 412
13.7%
18 409
13.6%
ValueCountFrequency (%)
13 433
14.4%
14 427
14.2%
15 431
14.4%
16 467
15.6%
17 412
13.7%
18 409
13.6%
19 421
14.0%
ValueCountFrequency (%)
19 421
14.0%
18 409
13.6%
17 412
13.7%
16 467
15.6%
15 431
14.4%
14 427
14.2%
13 433
14.4%

Gender
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size181.8 KiB
Male
1016 
Female
1007 
Other
977 

Length

Max length6
Median length5
Mean length4.997
Min length4

Characters and Unicode

Total characters14991
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowOther
4th rowFemale
5th rowOther

Common Values

ValueCountFrequency (%)
Male 1016
33.9%
Female 1007
33.6%
Other 977
32.6%

Length

2025-07-19T05:44:56.663337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-19T05:44:56.838264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 1016
33.9%
female 1007
33.6%
other 977
32.6%

Most occurring characters

ValueCountFrequency (%)
e 4007
26.7%
a 2023
13.5%
l 2023
13.5%
M 1016
 
6.8%
F 1007
 
6.7%
m 1007
 
6.7%
O 977
 
6.5%
t 977
 
6.5%
h 977
 
6.5%
r 977
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4007
26.7%
a 2023
13.5%
l 2023
13.5%
M 1016
 
6.8%
F 1007
 
6.7%
m 1007
 
6.7%
O 977
 
6.5%
t 977
 
6.5%
h 977
 
6.5%
r 977
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4007
26.7%
a 2023
13.5%
l 2023
13.5%
M 1016
 
6.8%
F 1007
 
6.7%
m 1007
 
6.7%
O 977
 
6.5%
t 977
 
6.5%
h 977
 
6.5%
r 977
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4007
26.7%
a 2023
13.5%
l 2023
13.5%
M 1016
 
6.8%
F 1007
 
6.7%
m 1007
 
6.7%
O 977
 
6.5%
t 977
 
6.5%
h 977
 
6.5%
r 977
 
6.5%
Distinct2726
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Memory size202.5 KiB
2025-07-19T05:44:57.295613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length20
Mean length12.066333
Min length6

Characters and Unicode

Total characters36199
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2500 ?
Unique (%)83.3%

Sample

1st rowHansonfort
2nd rowTheodorefort
3rd rowLindseystad
4th rowWest Anthony
5th rowPort Lindsaystad
ValueCountFrequency (%)
north 232
 
5.1%
lake 228
 
5.0%
east 222
 
4.9%
port 219
 
4.8%
south 215
 
4.7%
new 214
 
4.7%
west 205
 
4.5%
michael 25
 
0.6%
david 13
 
0.3%
john 11
 
0.2%
Other values (2217) 2951
65.1%
2025-07-19T05:44:58.048466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3390
 
9.4%
a 2947
 
8.1%
t 2823
 
7.8%
r 2772
 
7.7%
o 2439
 
6.7%
h 2056
 
5.7%
n 1938
 
5.4%
i 1745
 
4.8%
s 1723
 
4.8%
1535
 
4.2%
Other values (40) 12831
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36199
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3390
 
9.4%
a 2947
 
8.1%
t 2823
 
7.8%
r 2772
 
7.7%
o 2439
 
6.7%
h 2056
 
5.7%
n 1938
 
5.4%
i 1745
 
4.8%
s 1723
 
4.8%
1535
 
4.2%
Other values (40) 12831
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36199
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3390
 
9.4%
a 2947
 
8.1%
t 2823
 
7.8%
r 2772
 
7.7%
o 2439
 
6.7%
h 2056
 
5.7%
n 1938
 
5.4%
i 1745
 
4.8%
s 1723
 
4.8%
1535
 
4.2%
Other values (40) 12831
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36199
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3390
 
9.4%
a 2947
 
8.1%
t 2823
 
7.8%
r 2772
 
7.7%
o 2439
 
6.7%
h 2056
 
5.7%
n 1938
 
5.4%
i 1745
 
4.8%
s 1723
 
4.8%
1535
 
4.2%
Other values (40) 12831
35.4%

School_Grade
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size177.4 KiB
12th
529 
9th
526 
7th
497 
10th
487 
8th
482 

Length

Max length4
Median length3
Mean length3.4983333
Min length3

Characters and Unicode

Total characters10495
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9th
2nd row7th
3rd row11th
4th row12th
5th row9th

Common Values

ValueCountFrequency (%)
12th 529
17.6%
9th 526
17.5%
7th 497
16.6%
10th 487
16.2%
8th 482
16.1%
11th 479
16.0%

Length

2025-07-19T05:44:58.272165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-19T05:44:58.479125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12th 529
17.6%
9th 526
17.5%
7th 497
16.6%
10th 487
16.2%
8th 482
16.1%
11th 479
16.0%

Most occurring characters

ValueCountFrequency (%)
t 3000
28.6%
h 3000
28.6%
1 1974
18.8%
2 529
 
5.0%
9 526
 
5.0%
7 497
 
4.7%
0 487
 
4.6%
8 482
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 3000
28.6%
h 3000
28.6%
1 1974
18.8%
2 529
 
5.0%
9 526
 
5.0%
7 497
 
4.7%
0 487
 
4.6%
8 482
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 3000
28.6%
h 3000
28.6%
1 1974
18.8%
2 529
 
5.0%
9 526
 
5.0%
7 497
 
4.7%
0 487
 
4.6%
8 482
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 3000
28.6%
h 3000
28.6%
1 1974
18.8%
2 529
 
5.0%
9 526
 
5.0%
7 497
 
4.7%
0 487
 
4.6%
8 482
 
4.6%

Daily_Usage_Hours
Real number (ℝ)

High correlation 

Distinct107
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0206667
Minimum0
Maximum11.5
Zeros25
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:44:58.782899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8
Q13.7
median5
Q36.4
95-th percentile8.3
Maximum11.5
Range11.5
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation1.9565008
Coefficient of variation (CV)0.38968945
Kurtosis-0.12081611
Mean5.0206667
Median Absolute Deviation (MAD)1.3
Skewness0.016205397
Sum15062
Variance3.8278955
MonotonicityNot monotonic
2025-07-19T05:44:59.275412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4 74
 
2.5%
4.9 73
 
2.4%
4.3 69
 
2.3%
5.3 65
 
2.2%
5 64
 
2.1%
5.4 63
 
2.1%
4.1 62
 
2.1%
5.2 62
 
2.1%
4.7 62
 
2.1%
6.2 62
 
2.1%
Other values (97) 2344
78.1%
ValueCountFrequency (%)
0 25
0.8%
0.1 1
 
< 0.1%
0.2 3
 
0.1%
0.3 5
 
0.2%
0.4 4
 
0.1%
0.5 2
 
0.1%
0.6 4
 
0.1%
0.7 1
 
< 0.1%
0.8 7
 
0.2%
0.9 10
 
0.3%
ValueCountFrequency (%)
11.5 1
 
< 0.1%
11.2 1
 
< 0.1%
11 2
0.1%
10.9 1
 
< 0.1%
10.6 4
0.1%
10.5 1
 
< 0.1%
10.3 4
0.1%
10.2 3
0.1%
10.1 1
 
< 0.1%
9.8 2
0.1%

Sleep_Hours
Real number (ℝ)

Distinct71
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4897667
Minimum3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:44:59.588894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15.5
median6.5
Q37.5
95-th percentile9
Maximum10
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4907133
Coefficient of variation (CV)0.22970214
Kurtosis-0.29544149
Mean6.4897667
Median Absolute Deviation (MAD)1
Skewness0.011854251
Sum19469.3
Variance2.222226
MonotonicityNot monotonic
2025-07-19T05:44:59.911827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1 89
 
3.0%
7.1 87
 
2.9%
6.5 84
 
2.8%
6.8 83
 
2.8%
5.8 81
 
2.7%
6.9 81
 
2.7%
6.6 78
 
2.6%
6.3 77
 
2.6%
6.2 75
 
2.5%
6.7 74
 
2.5%
Other values (61) 2191
73.0%
ValueCountFrequency (%)
3 37
1.2%
3.1 8
 
0.3%
3.2 6
 
0.2%
3.3 10
 
0.3%
3.4 13
 
0.4%
3.5 9
 
0.3%
3.6 15
0.5%
3.7 5
 
0.2%
3.8 20
0.7%
3.9 24
0.8%
ValueCountFrequency (%)
10 36
1.2%
9.9 9
 
0.3%
9.8 8
 
0.3%
9.7 11
 
0.4%
9.6 4
 
0.1%
9.5 13
 
0.4%
9.4 7
 
0.2%
9.3 18
0.6%
9.2 17
0.6%
9.1 21
0.7%

Academic_Performance
Real number (ℝ)

Distinct51
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.947333
Minimum50
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:00.213202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile52
Q162
median75
Q388
95-th percentile98
Maximum100
Range50
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.684156
Coefficient of variation (CV)0.19592633
Kurtosis-1.2057006
Mean74.947333
Median Absolute Deviation (MAD)13
Skewness-0.0036031823
Sum224842
Variance215.62443
MonotonicityNot monotonic
2025-07-19T05:45:00.538270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 80
 
2.7%
98 72
 
2.4%
62 69
 
2.3%
59 69
 
2.3%
82 68
 
2.3%
57 68
 
2.3%
84 66
 
2.2%
75 66
 
2.2%
77 66
 
2.2%
85 65
 
2.2%
Other values (41) 2311
77.0%
ValueCountFrequency (%)
50 62
2.1%
51 64
2.1%
52 50
1.7%
53 54
1.8%
54 55
1.8%
55 56
1.9%
56 58
1.9%
57 68
2.3%
58 62
2.1%
59 69
2.3%
ValueCountFrequency (%)
100 56
1.9%
99 44
1.5%
98 72
2.4%
97 56
1.9%
96 60
2.0%
95 62
2.1%
94 43
1.4%
93 80
2.7%
92 46
1.5%
91 54
1.8%

Social_Interactions
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0976667
Minimum0
Maximum10
Zeros257
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:00.773758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1393332
Coefficient of variation (CV)0.61583729
Kurtosis-1.207441
Mean5.0976667
Median Absolute Deviation (MAD)3
Skewness-0.054093172
Sum15293
Variance9.855413
MonotonicityNot monotonic
2025-07-19T05:45:00.982629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 318
10.6%
5 284
9.5%
9 276
9.2%
6 275
9.2%
4 272
9.1%
3 268
8.9%
1 268
8.9%
10 267
8.9%
7 267
8.9%
0 257
8.6%
ValueCountFrequency (%)
0 257
8.6%
1 268
8.9%
2 248
8.3%
3 268
8.9%
4 272
9.1%
5 284
9.5%
6 275
9.2%
7 267
8.9%
8 318
10.6%
9 276
9.2%
ValueCountFrequency (%)
10 267
8.9%
9 276
9.2%
8 318
10.6%
7 267
8.9%
6 275
9.2%
5 284
9.5%
4 272
9.1%
3 268
8.9%
2 248
8.3%
1 268
8.9%

Exercise_Hours
Real number (ℝ)

Zeros 

Distinct39
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0406667
Minimum0
Maximum4
Zeros366
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:01.239909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1
Q31.5
95-th percentile2.3
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7346197
Coefficient of variation (CV)0.70591259
Kurtosis-0.257488
Mean1.0406667
Median Absolute Deviation (MAD)0.5
Skewness0.4386385
Sum3122
Variance0.53966611
MonotonicityNot monotonic
2025-07-19T05:45:01.523364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 366
 
12.2%
1.1 162
 
5.4%
0.9 162
 
5.4%
1 153
 
5.1%
0.8 145
 
4.8%
1.3 141
 
4.7%
1.4 129
 
4.3%
1.5 127
 
4.2%
1.2 127
 
4.2%
0.6 126
 
4.2%
Other values (29) 1362
45.4%
ValueCountFrequency (%)
0 366
12.2%
0.1 85
 
2.8%
0.2 86
 
2.9%
0.3 90
 
3.0%
0.4 122
 
4.1%
0.5 120
 
4.0%
0.6 126
 
4.2%
0.7 120
 
4.0%
0.8 145
 
4.8%
0.9 162
5.4%
ValueCountFrequency (%)
4 1
 
< 0.1%
3.7 1
 
< 0.1%
3.6 3
 
0.1%
3.5 1
 
< 0.1%
3.4 5
0.2%
3.3 3
 
0.1%
3.2 1
 
< 0.1%
3.1 2
 
0.1%
3 10
0.3%
2.9 5
0.2%

Anxiety_Level
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.59
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:01.762776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8906779
Coefficient of variation (CV)0.5171159
Kurtosis-1.2275319
Mean5.59
Median Absolute Deviation (MAD)3
Skewness-0.040637603
Sum16770
Variance8.3560187
MonotonicityNot monotonic
2025-07-19T05:45:01.960608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 322
10.7%
9 321
10.7%
6 305
10.2%
5 303
10.1%
4 300
10.0%
2 298
9.9%
1 296
9.9%
7 296
9.9%
8 295
9.8%
3 264
8.8%
ValueCountFrequency (%)
1 296
9.9%
2 298
9.9%
3 264
8.8%
4 300
10.0%
5 303
10.1%
6 305
10.2%
7 296
9.9%
8 295
9.8%
9 321
10.7%
10 322
10.7%
ValueCountFrequency (%)
10 322
10.7%
9 321
10.7%
8 295
9.8%
7 296
9.9%
6 305
10.2%
5 303
10.1%
4 300
10.0%
3 264
8.8%
2 298
9.9%
1 296
9.9%

Depression_Level
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4603333
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:02.152012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8715574
Coefficient of variation (CV)0.52589416
Kurtosis-1.2050111
Mean5.4603333
Median Absolute Deviation (MAD)2
Skewness0.0053720255
Sum16381
Variance8.2458418
MonotonicityNot monotonic
2025-07-19T05:45:02.354692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 328
10.9%
6 315
10.5%
4 312
10.4%
5 304
10.1%
9 303
10.1%
7 298
9.9%
3 290
9.7%
10 290
9.7%
8 282
9.4%
2 278
9.3%
ValueCountFrequency (%)
1 328
10.9%
2 278
9.3%
3 290
9.7%
4 312
10.4%
5 304
10.1%
6 315
10.5%
7 298
9.9%
8 282
9.4%
9 303
10.1%
10 290
9.7%
ValueCountFrequency (%)
10 290
9.7%
9 303
10.1%
8 282
9.4%
7 298
9.9%
6 315
10.5%
5 304
10.1%
4 312
10.4%
3 290
9.7%
2 278
9.3%
1 328
10.9%

Self_Esteem
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5463333
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:02.558378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8607542
Coefficient of variation (CV)0.51579197
Kurtosis-1.2105869
Mean5.5463333
Median Absolute Deviation (MAD)2
Skewness-0.050609824
Sum16639
Variance8.1839145
MonotonicityNot monotonic
2025-07-19T05:45:02.749240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 336
11.2%
6 321
10.7%
9 319
10.6%
1 310
10.3%
3 305
10.2%
8 295
9.8%
10 285
9.5%
4 280
9.3%
5 279
9.3%
2 270
9.0%
ValueCountFrequency (%)
1 310
10.3%
2 270
9.0%
3 305
10.2%
4 280
9.3%
5 279
9.3%
6 321
10.7%
7 336
11.2%
8 295
9.8%
9 319
10.6%
10 285
9.5%
ValueCountFrequency (%)
10 285
9.5%
9 319
10.6%
8 295
9.8%
7 336
11.2%
6 321
10.7%
5 279
9.3%
4 280
9.3%
3 305
10.2%
2 270
9.0%
1 310
10.3%

Parental_Control
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size170.0 KiB
1
1522 
0
1478 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1522
50.7%
0 1478
49.3%

Length

2025-07-19T05:45:02.999661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-19T05:45:03.161699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1522
50.7%
0 1478
49.3%

Most occurring characters

ValueCountFrequency (%)
1 1522
50.7%
0 1478
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1522
50.7%
0 1478
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1522
50.7%
0 1478
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1522
50.7%
0 1478
49.3%

Screen_Time_Before_Bed
Real number (ℝ)

Zeros 

Distinct27
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0067333
Minimum0
Maximum2.6
Zeros89
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:03.345976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.7
median1
Q31.4
95-th percentile1.8
Maximum2.6
Range2.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.49287825
Coefficient of variation (CV)0.48958174
Kurtosis-0.34892062
Mean1.0067333
Median Absolute Deviation (MAD)0.3
Skewness0.098428526
Sum3020.2
Variance0.24292897
MonotonicityNot monotonic
2025-07-19T05:45:03.598471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 233
 
7.8%
1.1 229
 
7.6%
0.8 229
 
7.6%
0.9 218
 
7.3%
1.2 201
 
6.7%
0.7 199
 
6.6%
1.3 198
 
6.6%
0.6 194
 
6.5%
1.4 173
 
5.8%
1.5 172
 
5.7%
Other values (17) 954
31.8%
ValueCountFrequency (%)
0 89
 
3.0%
0.1 47
 
1.6%
0.2 66
 
2.2%
0.3 88
 
2.9%
0.4 109
3.6%
0.5 143
4.8%
0.6 194
6.5%
0.7 199
6.6%
0.8 229
7.6%
0.9 218
7.3%
ValueCountFrequency (%)
2.6 1
 
< 0.1%
2.5 2
 
0.1%
2.4 8
 
0.3%
2.3 4
 
0.1%
2.2 14
 
0.5%
2.1 28
 
0.9%
2 31
 
1.0%
1.9 49
1.6%
1.8 61
2.0%
1.7 91
3.0%

Phone_Checks_Per_Day
Real number (ℝ)

Distinct131
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.093
Minimum20
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:03.899475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q151
median82
Q3115.25
95-th percentile144
Maximum150
Range130
Interquartile range (IQR)64.25

Descriptive statistics

Standard deviation37.747044
Coefficient of variation (CV)0.45427465
Kurtosis-1.1737317
Mean83.093
Median Absolute Deviation (MAD)32
Skewness0.066851603
Sum249279
Variance1424.8393
MonotonicityNot monotonic
2025-07-19T05:45:04.210133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 35
 
1.2%
67 34
 
1.1%
58 32
 
1.1%
26 32
 
1.1%
22 32
 
1.1%
129 31
 
1.0%
119 31
 
1.0%
76 31
 
1.0%
83 31
 
1.0%
32 30
 
1.0%
Other values (121) 2681
89.4%
ValueCountFrequency (%)
20 28
0.9%
21 25
0.8%
22 32
1.1%
23 18
0.6%
24 29
1.0%
25 23
0.8%
26 32
1.1%
27 21
0.7%
28 21
0.7%
29 25
0.8%
ValueCountFrequency (%)
150 19
0.6%
149 21
0.7%
148 20
0.7%
147 16
0.5%
146 27
0.9%
145 19
0.6%
144 30
1.0%
143 22
0.7%
142 20
0.7%
141 29
1.0%

Apps_Used_Daily
Real number (ℝ)

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.609333
Minimum5
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:04.440445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q19
median13
Q317
95-th percentile20
Maximum20
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6114858
Coefficient of variation (CV)0.36572003
Kurtosis-1.2125256
Mean12.609333
Median Absolute Deviation (MAD)4
Skewness-0.035907635
Sum37828
Variance21.265801
MonotonicityNot monotonic
2025-07-19T05:45:04.663038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
14 202
 
6.7%
6 200
 
6.7%
19 198
 
6.6%
17 198
 
6.6%
18 195
 
6.5%
11 195
 
6.5%
12 193
 
6.4%
16 191
 
6.4%
20 188
 
6.3%
7 184
 
6.1%
Other values (6) 1056
35.2%
ValueCountFrequency (%)
5 173
5.8%
6 200
6.7%
7 184
6.1%
8 176
5.9%
9 175
5.8%
10 173
5.8%
11 195
6.5%
12 193
6.4%
13 183
6.1%
14 202
6.7%
ValueCountFrequency (%)
20 188
6.3%
19 198
6.6%
18 195
6.5%
17 198
6.6%
16 191
6.4%
15 176
5.9%
14 202
6.7%
13 183
6.1%
12 193
6.4%
11 195
6.5%

Time_on_Social_Media
Real number (ℝ)

Distinct51
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4992333
Minimum0
Maximum5
Zeros21
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:05.108608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9
Q11.8
median2.5
Q33.2
95-th percentile4.2
Maximum5
Range5
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation0.98820119
Coefficient of variation (CV)0.39540173
Kurtosis-0.2132298
Mean2.4992333
Median Absolute Deviation (MAD)0.7
Skewness0.066972991
Sum7497.7
Variance0.97654159
MonotonicityNot monotonic
2025-07-19T05:45:05.431048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 127
 
4.2%
2.2 126
 
4.2%
2.8 122
 
4.1%
2.6 118
 
3.9%
2.5 116
 
3.9%
3 113
 
3.8%
2.3 112
 
3.7%
1.9 112
 
3.7%
2.1 111
 
3.7%
1.8 107
 
3.6%
Other values (41) 1836
61.2%
ValueCountFrequency (%)
0 21
0.7%
0.1 1
 
< 0.1%
0.2 9
 
0.3%
0.3 7
 
0.2%
0.4 14
0.5%
0.5 17
0.6%
0.6 15
0.5%
0.7 26
0.9%
0.8 28
0.9%
0.9 31
1.0%
ValueCountFrequency (%)
5 27
0.9%
4.9 13
0.4%
4.8 8
 
0.3%
4.7 7
 
0.2%
4.6 13
0.4%
4.5 15
0.5%
4.4 23
0.8%
4.3 24
0.8%
4.2 25
0.8%
4.1 31
1.0%

Time_on_Gaming
Real number (ℝ)

Zeros 

Distinct41
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5252667
Minimum0
Maximum4
Zeros205
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:05.698028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8
median1.5
Q32.2
95-th percentile3.1
Maximum4
Range4
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation0.93270122
Coefficient of variation (CV)0.61150043
Kurtosis-0.49820636
Mean1.5252667
Median Absolute Deviation (MAD)0.7
Skewness0.25901082
Sum4575.8
Variance0.86993157
MonotonicityNot monotonic
2025-07-19T05:45:06.007574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 205
 
6.8%
1.7 140
 
4.7%
1.6 131
 
4.4%
1.2 130
 
4.3%
1 122
 
4.1%
1.4 122
 
4.1%
1.5 120
 
4.0%
1.3 120
 
4.0%
1.8 110
 
3.7%
1.9 103
 
3.4%
Other values (31) 1697
56.6%
ValueCountFrequency (%)
0 205
6.8%
0.1 52
 
1.7%
0.2 59
 
2.0%
0.3 62
 
2.1%
0.4 65
 
2.2%
0.5 67
 
2.2%
0.6 83
2.8%
0.7 89
3.0%
0.8 82
 
2.7%
0.9 90
3.0%
ValueCountFrequency (%)
4 25
0.8%
3.9 2
 
0.1%
3.8 7
 
0.2%
3.7 12
 
0.4%
3.6 15
 
0.5%
3.5 10
 
0.3%
3.4 16
 
0.5%
3.3 26
0.9%
3.2 29
1.0%
3.1 48
1.6%

Time_on_Education
Real number (ℝ)

Zeros 

Distinct31
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0163333
Minimum0
Maximum3
Zeros250
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:06.248773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1
Q31.5
95-th percentile2.105
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.64834148
Coefficient of variation (CV)0.63792208
Kurtosis-0.3986376
Mean1.0163333
Median Absolute Deviation (MAD)0.5
Skewness0.34019802
Sum3049
Variance0.42034667
MonotonicityNot monotonic
2025-07-19T05:45:06.503852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 250
 
8.3%
0.8 194
 
6.5%
0.7 184
 
6.1%
1 182
 
6.1%
1.1 164
 
5.5%
0.9 160
 
5.3%
1.2 157
 
5.2%
1.4 144
 
4.8%
0.5 139
 
4.6%
1.3 139
 
4.6%
Other values (21) 1287
42.9%
ValueCountFrequency (%)
0 250
8.3%
0.1 74
 
2.5%
0.2 96
 
3.2%
0.3 105
3.5%
0.4 120
4.0%
0.5 139
4.6%
0.6 123
4.1%
0.7 184
6.1%
0.8 194
6.5%
0.9 160
5.3%
ValueCountFrequency (%)
3 10
 
0.3%
2.9 3
 
0.1%
2.8 6
 
0.2%
2.7 4
 
0.1%
2.6 17
 
0.6%
2.5 13
 
0.4%
2.4 24
0.8%
2.3 30
1.0%
2.2 43
1.4%
2.1 45
1.5%
Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size190.4 KiB
Browsing
627 
Other
622 
Education
602 
Social Media
575 
Gaming
574 

Length

Max length12
Median length8
Mean length7.9626667
Min length5

Characters and Unicode

Total characters23888
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrowsing
2nd rowBrowsing
3rd rowEducation
4th rowSocial Media
5th rowGaming

Common Values

ValueCountFrequency (%)
Browsing 627
20.9%
Other 622
20.7%
Education 602
20.1%
Social Media 575
19.2%
Gaming 574
19.1%

Length

2025-07-19T05:45:06.778977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-19T05:45:06.989538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
browsing 627
17.5%
other 622
17.4%
education 602
16.8%
social 575
16.1%
media 575
16.1%
gaming 574
16.1%

Most occurring characters

ValueCountFrequency (%)
i 2953
 
12.4%
a 2326
 
9.7%
o 1804
 
7.6%
n 1803
 
7.5%
r 1249
 
5.2%
t 1224
 
5.1%
g 1201
 
5.0%
e 1197
 
5.0%
c 1177
 
4.9%
d 1177
 
4.9%
Other values (13) 7777
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23888
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2953
 
12.4%
a 2326
 
9.7%
o 1804
 
7.6%
n 1803
 
7.5%
r 1249
 
5.2%
t 1224
 
5.1%
g 1201
 
5.0%
e 1197
 
5.0%
c 1177
 
4.9%
d 1177
 
4.9%
Other values (13) 7777
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23888
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2953
 
12.4%
a 2326
 
9.7%
o 1804
 
7.6%
n 1803
 
7.5%
r 1249
 
5.2%
t 1224
 
5.1%
g 1201
 
5.0%
e 1197
 
5.0%
c 1177
 
4.9%
d 1177
 
4.9%
Other values (13) 7777
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23888
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2953
 
12.4%
a 2326
 
9.7%
o 1804
 
7.6%
n 1803
 
7.5%
r 1249
 
5.2%
t 1224
 
5.1%
g 1201
 
5.0%
e 1197
 
5.0%
c 1177
 
4.9%
d 1177
 
4.9%
Other values (13) 7777
32.6%

Family_Communication
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4596667
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:07.222006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8645724
Coefficient of variation (CV)0.52467899
Kurtosis-1.2183541
Mean5.4596667
Median Absolute Deviation (MAD)2
Skewness0.011521332
Sum16379
Variance8.2057751
MonotonicityNot monotonic
2025-07-19T05:45:07.418801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 313
10.4%
7 312
10.4%
4 305
10.2%
2 305
10.2%
1 305
10.2%
3 304
10.1%
6 298
9.9%
10 294
9.8%
5 290
9.7%
9 274
9.1%
ValueCountFrequency (%)
1 305
10.2%
2 305
10.2%
3 304
10.1%
4 305
10.2%
5 290
9.7%
6 298
9.9%
7 312
10.4%
8 313
10.4%
9 274
9.1%
10 294
9.8%
ValueCountFrequency (%)
10 294
9.8%
9 274
9.1%
8 313
10.4%
7 312
10.4%
6 298
9.9%
5 290
9.7%
4 305
10.2%
3 304
10.1%
2 305
10.2%
1 305
10.2%

Weekend_Usage_Hours
Real number (ℝ)

Distinct120
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0151
Minimum0
Maximum14
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:07.675853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.7
Q14.7
median6
Q37.4
95-th percentile9.4
Maximum14
Range14
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation2.0147759
Coefficient of variation (CV)0.33495301
Kurtosis-0.063614564
Mean6.0151
Median Absolute Deviation (MAD)1.3
Skewness0.048642576
Sum18045.3
Variance4.0593218
MonotonicityNot monotonic
2025-07-19T05:45:07.999522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.2 70
 
2.3%
5.7 69
 
2.3%
5.3 64
 
2.1%
6.8 64
 
2.1%
6.6 63
 
2.1%
6.5 62
 
2.1%
4.9 61
 
2.0%
6.3 60
 
2.0%
7.1 60
 
2.0%
5 59
 
2.0%
Other values (110) 2368
78.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
0.3 2
0.1%
0.4 1
 
< 0.1%
0.7 4
0.1%
0.9 3
0.1%
1 4
0.1%
1.1 3
0.1%
1.2 3
0.1%
ValueCountFrequency (%)
14 1
< 0.1%
12.6 1
< 0.1%
12.4 1
< 0.1%
12.3 2
0.1%
12 2
0.1%
11.8 1
< 0.1%
11.6 1
< 0.1%
11.5 2
0.1%
11.4 1
< 0.1%
11.3 1
< 0.1%

Addiction_Level
Real number (ℝ)

High correlation 

Distinct80
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8819
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-07-19T05:45:08.313029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.5
Q18
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6095981
Coefficient of variation (CV)0.18122227
Kurtosis1.9027804
Mean8.8819
Median Absolute Deviation (MAD)0
Skewness-1.54197
Sum26645.7
Variance2.590806
MonotonicityNot monotonic
2025-07-19T05:45:08.623712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1524
50.8%
9.9 50
 
1.7%
9.6 46
 
1.5%
8 45
 
1.5%
7.8 45
 
1.5%
9.4 44
 
1.5%
9.5 44
 
1.5%
9.3 43
 
1.4%
9 41
 
1.4%
8.4 41
 
1.4%
Other values (70) 1077
35.9%
ValueCountFrequency (%)
1 1
< 0.1%
1.4 2
0.1%
2 1
< 0.1%
2.1 2
0.1%
2.2 1
< 0.1%
2.3 1
< 0.1%
2.4 1
< 0.1%
2.6 1
< 0.1%
2.8 2
0.1%
3 2
0.1%
ValueCountFrequency (%)
10 1524
50.8%
9.9 50
 
1.7%
9.8 28
 
0.9%
9.7 37
 
1.2%
9.6 46
 
1.5%
9.5 44
 
1.5%
9.4 44
 
1.5%
9.3 43
 
1.4%
9.2 34
 
1.1%
9.1 35
 
1.2%

Interactions

2025-07-19T05:44:48.810054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:28.194577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:32.604221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:37.246885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:41.510237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:46.342073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:51.074857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:55.502494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:00.273125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:04.384093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:08.489645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:12.763330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:17.015022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:21.591206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:26.302433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:30.568987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:35.324454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:40.023427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:44.303854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:49.018605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:28.406779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:32.828970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:37.469317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-19T05:44:08.697971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-19T05:44:49.336627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:28.642385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-19T05:43:29.540182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-19T05:44:25.443188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:29.880368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:34.621534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:39.182417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:43.673629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:48.106527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:52.727008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:32.133002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:36.777965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:41.078457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:45.893320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:50.626558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:55.073242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:59.612266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:03.811905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:08.008245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:12.364708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:16.569360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:21.139974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:25.847745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:30.136721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:34.851222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:39.421255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:43.873968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:48.362361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:52.950745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:32.370358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:37.025106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:41.306386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:46.135608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:50.858947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:43:55.297516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:00.037180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:04.055458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:08.259108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:12.585465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:16.808689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:21.374022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:26.082573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:30.369726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:35.107134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:39.758045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:44.104126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-19T05:44:48.602648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-19T05:45:08.906930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Academic_PerformanceAddiction_LevelAgeAnxiety_LevelApps_Used_DailyDaily_Usage_HoursDepression_LevelExercise_HoursFamily_CommunicationGenderIDParental_ControlPhone_Checks_Per_DayPhone_Usage_PurposeSchool_GradeScreen_Time_Before_BedSelf_EsteemSleep_HoursSocial_InteractionsTime_on_EducationTime_on_GamingTime_on_Social_MediaWeekend_Usage_Hours
Academic_Performance1.0000.0140.0230.003-0.0260.021-0.0270.002-0.0280.000-0.0130.039-0.0170.0280.030-0.004-0.0060.0020.0110.024-0.0290.0410.015
Addiction_Level0.0141.0000.0250.0190.3250.6390.002-0.018-0.0020.0280.0010.0000.2490.0090.0180.014-0.023-0.218-0.023-0.0020.2730.3180.006
Age0.0230.0251.0000.015-0.0030.0450.0520.008-0.0030.006-0.0360.000-0.0090.0120.0210.006-0.0330.021-0.021-0.000-0.010-0.001-0.001
Anxiety_Level0.0030.0190.0151.0000.007-0.0020.0190.0030.0120.0000.0090.0000.0180.0000.000-0.0040.0050.0080.0040.0270.0180.001-0.001
Apps_Used_Daily-0.0260.325-0.0030.0071.0000.0210.019-0.006-0.0060.000-0.0050.000-0.0080.0260.0150.027-0.0280.026-0.026-0.0000.0100.028-0.027
Daily_Usage_Hours0.0210.6390.045-0.0020.0211.0000.013-0.0100.0010.018-0.0010.0600.0070.0250.0130.012-0.0010.011-0.0280.004-0.007-0.0120.025
Depression_Level-0.0270.0020.0520.0190.0190.0131.000-0.017-0.0130.0000.0050.000-0.0020.0000.000-0.031-0.027-0.0110.024-0.030-0.0100.000-0.013
Exercise_Hours0.002-0.0180.0080.003-0.006-0.010-0.0171.0000.0090.000-0.0010.061-0.0090.0330.0000.015-0.0140.0080.0050.019-0.015-0.0170.040
Family_Communication-0.028-0.002-0.0030.012-0.0060.001-0.0130.0091.0000.035-0.0190.000-0.0020.0040.0000.013-0.025-0.0280.0040.0050.025-0.0230.029
Gender0.0000.0280.0060.0000.0000.0180.0000.0000.0351.0000.0230.0000.0000.0330.0000.0000.0240.0000.0000.0480.0570.0350.000
ID-0.0130.001-0.0360.009-0.005-0.0010.005-0.001-0.0190.0231.0000.061-0.0370.0340.0000.007-0.0070.020-0.0420.0020.0180.0240.022
Parental_Control0.0390.0000.0000.0000.0000.0600.0000.0610.0000.0000.0611.0000.0320.0260.0000.0270.0000.0000.0210.0200.0000.0330.000
Phone_Checks_Per_Day-0.0170.249-0.0090.018-0.0080.007-0.002-0.009-0.0020.000-0.0370.0321.0000.0000.0000.012-0.0060.0010.012-0.0260.0100.005-0.025
Phone_Usage_Purpose0.0280.0090.0120.0000.0260.0250.0000.0330.0040.0330.0340.0260.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.025
School_Grade0.0300.0180.0210.0000.0150.0130.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0080.0250.0210.0320.0230.0320.010
Screen_Time_Before_Bed-0.0040.0140.006-0.0040.0270.012-0.0310.0150.0130.0000.0070.0270.0120.0000.0001.000-0.001-0.003-0.0190.0340.009-0.0060.042
Self_Esteem-0.006-0.023-0.0330.005-0.028-0.001-0.027-0.014-0.0250.024-0.0070.000-0.0060.0000.008-0.0011.0000.0130.008-0.010-0.013-0.007-0.043
Sleep_Hours0.002-0.2180.0210.0080.0260.011-0.0110.008-0.0280.0000.0200.0000.0010.0000.025-0.0030.0131.000-0.016-0.0100.008-0.011-0.003
Social_Interactions0.011-0.023-0.0210.004-0.026-0.0280.0240.0050.0040.000-0.0420.0210.0120.0000.021-0.0190.008-0.0161.000-0.009-0.0080.004-0.021
Time_on_Education0.024-0.002-0.0000.027-0.0000.004-0.0300.0190.0050.0480.0020.020-0.0260.0000.0320.034-0.010-0.010-0.0091.000-0.0020.010-0.037
Time_on_Gaming-0.0290.273-0.0100.0180.010-0.007-0.010-0.0150.0250.0570.0180.0000.0100.0000.0230.009-0.0130.008-0.008-0.0021.000-0.0180.034
Time_on_Social_Media0.0410.318-0.0010.0010.028-0.0120.000-0.017-0.0230.0350.0240.0330.0050.0000.032-0.006-0.007-0.0110.0040.010-0.0181.000-0.012
Weekend_Usage_Hours0.0150.006-0.001-0.001-0.0270.025-0.0130.0400.0290.0000.0220.000-0.0250.0250.0100.042-0.043-0.003-0.021-0.0370.034-0.0121.000

Missing values

2025-07-19T05:44:53.411739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-19T05:44:53.921434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDNameAgeGenderLocationSchool_GradeDaily_Usage_HoursSleep_HoursAcademic_PerformanceSocial_InteractionsExercise_HoursAnxiety_LevelDepression_LevelSelf_EsteemParental_ControlScreen_Time_Before_BedPhone_Checks_Per_DayApps_Used_DailyTime_on_Social_MediaTime_on_GamingTime_on_EducationPhone_Usage_PurposeFamily_CommunicationWeekend_Usage_HoursAddiction_Level
01Shannon Francis13FemaleHansonfort9th4.06.17850.1103801.486193.61.71.2Browsing48.710.0
12Scott Rodriguez17FemaleTheodorefort7th5.56.57050.037300.99691.14.01.8Browsing25.310.0
23Adrian Knox13OtherLindseystad11th5.85.59380.8231000.513780.31.50.4Education65.79.2
34Brittany Hamilton18FemaleWest Anthony12th3.13.97881.6910301.412873.11.60.8Social Media83.09.8
45Steven Smith14OtherPort Lindsaystad9th2.56.75641.115101.096202.60.91.1Gaming103.78.6
56Mary Adams13FemaleEast Angelachester10th3.96.38930.771301.113583.80.01.4Social Media76.08.8
67Hailey Moses16MaleNorth Jeffrey11th6.36.78930.967900.812991.82.71.0Education77.810.0
78Veronica Marshall13OtherJenniferport10th5.16.17022.256801.03472.31.60.5Browsing98.08.0
89Edward Avila13MaleLeebury8th3.09.17901.817600.970132.72.21.3Education109.17.3
910James Carter18OtherPrestonview11th3.95.88981.191900.9121132.70.41.0Other92.99.1
IDNameAgeGenderLocationSchool_GradeDaily_Usage_HoursSleep_HoursAcademic_PerformanceSocial_InteractionsExercise_HoursAnxiety_LevelDepression_LevelSelf_EsteemParental_ControlScreen_Time_Before_BedPhone_Checks_Per_DayApps_Used_DailyTime_on_Social_MediaTime_on_GamingTime_on_EducationPhone_Usage_PurposeFamily_CommunicationWeekend_Usage_HoursAddiction_Level
29902991Lauren Ballard13FemaleEast John10th2.85.69241.1710711.177103.73.40.7Social Media57.110.0
29912992Christina Davila16MaleHeatherfurt11th6.35.78782.961810.23652.12.32.0Social Media86.69.6
29922993Joseph Watkins15OtherEmilystad10th3.24.37182.166811.328104.90.00.5Browsing110.08.5
29932994Sandra Acevedo17OtherRobertsville12th9.06.98400.612710.62661.91.12.1Social Media27.410.0
29942995Calvin Larsen17FemaleMarieland12th3.79.66720.3106901.09571.30.01.1Social Media86.13.5
29952996Jesus Yates16FemaleNew Jennifer12th3.96.45340.9710210.380152.71.81.0Other89.49.8
29962997Bethany Murray13FemaleRichardport8th3.67.39350.088910.94583.10.00.3Gaming95.25.5
29972998Norman Hughes14OtherRebeccaton7th3.26.59810.043900.251132.40.22.4Social Media95.96.2
29982999Barbara Hinton17FemaleRamirezmouth9th6.77.56730.235901.6125171.72.61.5Browsing46.110.0
29993000Curtis Johnson17MaleLake Alexander10th3.56.97942.145510.611780.02.30.1Education75.16.3